
Dear All,
I hope this message finds you well. We are thrilled to extend opportunities to join our team at the Johns Hopkins University in developing a three-dimensional atlas of the human body, BodyMaps. We are actively seeking several skilled and passionate students to make substantial contributions to this program.
About the BodyMaps Program
BodyMaps is a rigorously mentored research program at the convergence of Artificial Intelligence (AI) and Medicine, hosted at the Computational Cognition, Vision, and Learning (CCVL) lab. It welcomes students, researchers, clinicians, and developers around the world. Over 9 to 12 months, candidates will lead high-impact research, receive training and working in small interdisciplinary teams.
BodyMaps AI Bootcamp
We can host visiting students from outside Hopkins. There are multiple training positions open for undergraduates, graduate students, and post-doctoral scholars:
Rolling applications, reviewed at the end of every month.
Mentors
Prof. Zongwei Zhou is the director of the BodyMaps program, and directly mentors each project. The program is overseen by highly qualified mentors who are experts in their respective fields. Each project is assigned one or more mentors, ensuring access to top-tier expertise and direct mentorship.
-
Zongwei Zhou, PhD | Assistant Research Professor | Johns Hopkins University
-
Alan Yuille, PhD | Bloomberg Distinguished Professor | Johns Hopkins University
-
Pedro R. A. S. Bassi, PhD | Postdoctoral Fellow | Johns Hopkins University
-
Heng Li, PhD | Associate Professor | Johns Hopkins Medicine
-
Kai Ding, PhD | Associate Professor | Johns Hopkins Medicine
-
Yang Yang, PhD | Associate Professor | University of California, San Francisco
-
Kang Wang, MD, PhD | Assistant Professor | University of California, San Francisco
-
Arkadiusz Sitek, PhD | Associate Professor | Harvard University
-
Yucheng Tang, PhD | Senior Research Scientist | Nvidia
-
Szymon Płotka, PhD | Assistant Professor | Jagiellonian University
JHU students can get research units with Prof. Zongwei Zhou.
The position can be either in-person or remote, though in-person participation is encouraged.
BodyMaps Demonstration Award
The BodyMaps Demonstration Award provides multiple one-year awards of up to $100,000 to support projects with strong potential to develop AI algorithms for early cancer detection from CT scans of the abdomen, pelvis, and chest. The program emphasizes multi-cancer early detection and prioritizes innovative approaches that demonstrate the potential to significantly outperform expert radiologist performance in both sensitivity and specificity.
All letter of intents should be submitted as PDFs here.
Thank you.
Previous Projects
November 24, 2025
CCVL researchers to present 18 abstracts at RSNA 2025
The Annual Meeting of the Radiological Society of North America is the premier, global radiology conference where the power of imaging, education, and collaboration come to life.
Contributed by many previous intern students

February 19, 2025
AbdomenAtlas: an AI-based approach for early cancer diagnosis
AbdomenAtlas is an extensive dataset of 3D reconstructed and annotated abdominal CT scans that can be used to train AI technology on identifying cancer.
Wenxuan Li, Master's student at Johns Hopkins University, 2023-2024

February 17, 2025
A Touchstone of Medical Artificial Intelligence
Johns Hopkins researchers release a new standard for evaluating medical AI models to promote fairness and reduce bias.
Pedro R. A. S. Bassi, PhD student at University of Bologna, 2024-2025

August 22, 2024
Forget-Me-Not: Selective Memory Can Help AI Remember More, Not Less
Inspired by human learning patterns, Johns Hopkins computer scientists have developed a new technique to train AI models on massive amounts of medical data without forgetting what they’ve already learned.
Yu-Cheng Chou, Master's student at Wuhan University, 2022-2023

May 30, 2024
Johns Hopkins Researchers Create Artificial Tumors to Help AI Detect Early-Stage Cancer
The Hopkins-led team demonstrated that an AI model trained solely on synthetic tumor data works as well as models trained on real tumors.
Qixin Hu, Master's student at Huazhong University of Science and Technology, 2022-2023

February 9, 2024
AI and Radiologists Unite to Map the Abdomen
Hopkins researchers have leveraged the synergy between medical professionals and artificial intelligence algorithms to create the largest annotated multi-organ dataset to date.
Chongyu Qu, Master's student at Johns Hopkins University, 2023-2024










